Method and system for preprocessing respiration monitoring signal in user moving scene

Abstract

The invention discloses a user mobile scene respiration monitoring signal preprocessing method and system. The method comprises the following steps: collecting signal data returned by an IR-UWB radar; compensating the difference by multiplying the attenuation estimation parameter to reduce the influence of the changing environment on the liquid dropping signal; an improved adaptive clutter removal algorithm is adopted to adaptively estimate a time-varying background condition so as to eliminate dynamic background reflection; eliminating distance space background reflection by applying a clustering algorithm on the distance dimension; calculating a cost matrix of the whole perception matrix, and representing the tracking of the affected interval as a directed probability graph model to find a candidate sequence of the affected interval; the respiratory frequency components in the candidate sequence are evaluated by multiplication in the frequency domain, and the most appropriate result is determined. According to the method, the respiration monitoring signals in the user moving scene are preprocessed; the method does not involve the privacy problem, does not need to deploy a sensor in advance, avoids the limitation of a user scene in an existing breathing signal preprocessing scheme, and achieves the high-precision recognition and recovery of the breathing signal.

Description

Background Art

[0002] In the field of wireless respiration monitoring, the monitoring technology based on radio frequency signals has attracted much attention in many fields such as healthcare, smart home, and intelligent cockpit due to its outstanding non-contact feature. Taking the medical scenario as an example, it can achieve real-time and non-contact monitoring of patients' respiration, reduce the discomfort of patients caused by wearing traditional monitoring devices, and provide more convenient and continuous health data for medical staff. In the smart home, this technology can be used to monitor the sleep respiration status of family members and achieve health warnings.

[0003] However, in the actual application process, this technology has encountered numerous challenges. There are significant differences in signals in different propagation environments. In terms of distance factors, the signal will have amplitude and phase changes as the propagation distance increases, and in a complex indoor environment, the signal is affected by reflections and refractions of surrounding objects, increasing the difficulty of signal processing. Moreover, the time variation cannot be ignored. Dynamic interferences may occur in the surrounding environment at any time, such as people walking, devices turning on and off, etc. These interferences will cause signal fluctuations and further increase the complexity of signal processing, making it difficult for existing monitoring methods to stably and accurately monitor respiration signals and unable to meet the requirements of accurate monitoring in actual applications. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for preprocessing respiration monitoring signals in a user movement scenario in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems of difficult signal processing and inaccurate extraction of respiration signals in the existing radio frequency signal-based respiration monitoring technology, effectively eliminate background noise, accurately identify, track, and restore signals affected by respiration, and improve the accuracy and stability of respiration monitoring.

[0005] The present invention adopts the following technical solutions:

[0006] A method for preprocessing respiration monitoring signals in a user movement scenario includes the following steps:

[0007] Collect signal data and form a signal matrix Y(t, i);

[0008] Preprocess the signal matrix Y(t, i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y ′ (t, i);

[0009] Perform DBSCAN clustering on each distance unit in the obtained signal matrix Y ′ (t, i), and subtract the average amplitude and average phase of the dominant category from the original signal matrix to eliminate background reflections in the distance space, obtaining a signal Y″(t, i);

[0010] Model the tracking problem of the affected distance units as a directed probabilistic graph model, generate a cost matrix for the obtained signal Y″(t,i), calculate the tracking problem, and generate the sequence l of the affected distance units c , for the sequence l of the affected distance units c Perform smoothing processing to obtain a set of sequences L of the affected distance units = {l1,…,l c ,…};

[0011] Extract three consecutive distance units from the obtained sequence L of the affected distance units and perform a fast Fourier transform, select the sequence l of the affected distance units with the largest product s , extract the waveform of the sequence l s and perform smoothing processing to obtain the preprocessed respiratory signal.

[0012] Preferably, collecting signal data and forming the signal matrix Y(t,i) is specifically:

[0013] Collect signal data using an IR-UWB radar, and compensate for the difference by multiplying the attenuation estimation parameter A c is the amplitude compensation estimation parameter between different distance units, j is the imaginary unit, and θ c is the phase compensation estimation parameter between different distance units, and the compensated signal Y′ t (i) is obtained, and finally the signal matrix Y(t,i) is obtained.

[0014] Preferably, the compensated signal matrix Y′ t (i) is:

[0015]

[0016] where Y t (i) represents the signal received in the t-th frame.

[0017] Preferably, the signal Y t (i) received in the t-th frame is:

[0018] Y t (i) = Y(t,i)| t＝t,i＝1→N

[0019] where N represents the total number of distance units, t is the index of the transmitted frame, and i represents the distance interval.

[0020] Preferably, performing DBSCAN clustering on each distance unit in the signal matrix Y ′ (t,i) is specifically:

[0021] Decompose the signal vector into an amplitude vector and a phase vector Perform DBSCAN clustering on these two vectors for each range bin, and calculate the average amplitude μ A and the average phase μ Φ ;

[0022] Then eliminate the background reflections in the range space, and obtain the signal matrix Y″ from the signal matrix Y * by eliminating the background reflections in the range domain.

[0023] Preferably, the average amplitude μ A and the average phase μ Φ are:

[0024]

[0025] where, and represent the dominant classes corresponding to the i-th range bin in the amplitude matrix A * and the phase matrix Φ * respectively, N represents the total number of range bins, represents the average amplitude of the data with the cluster label t when the range bin is i, represents the average phase of the data with the cluster label t when the range bin is i.

[0026] Preferably, the elimination of the background reflections in the range space is specifically as follows:

[0027]

[0028] where, ⊙ represents dot product, ∈ represents a small constant to prevent division by zero error, represents the signal after background elimination, Y * (t,i) is the signal obtained by using the improved adaptive clutter cancellation algorithm.

[0029] Preferably, the obtained signal Y″(t,i) is used to generate a cost matrix, specifically:

[0030] Model the tracking of the affected range bins as a directed probabilistic graph model, and select the next affected range bin through t and t + 1 are frame numbers, is the serial number of the next affected range bin, represents from the range bin i in the t-th frame t to the range bin i in the (t + 1)-th frame ′ of the cost matrix.

[0031] Preferably, the preprocessed respiratory signal is specifically:

[0032] Extract the respiratory waveform Then perform a fast Fourier transform; then extract the waveform of sequence l s and perform smoothing using a Butterworth filter to obtain the final preprocessed respiratory signal;

[0033] The fast Fourier transform is as follows:

[0034]

[0035] Among them, V represents the coefficient corresponding to respiration, represents the noise component.

[0036] In a second aspect, an embodiment of the present invention provides a preprocessing system for respiratory monitoring signals in a user movement scenario, including:

[0037] An acquisition module that acquires signal data and forms a signal matrix Y(t, i);

[0038] A preprocessing module that preprocesses the obtained signal matrix Y(t, i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y ′ (t, i);

[0039] A clustering module that performs DBSCAN clustering on each range cell in the signal matrix Y ′ (t, i), and subtracts the average amplitude and average phase of the dominant class from the original signal matrix to eliminate background reflections in the range space, obtaining a signal Y″(t, i);

[0040] A calculation module that models the tracking problem of the affected range cells as a directed probabilistic graph model, generates a cost matrix from the obtained signal Y″(t, i), calculates the tracking problem, and generates a sequence l of affected range cells c , and performs smoothing on the sequence l of affected range cells c to obtain a set of sequences L of affected range cells = {l1,..., l c ,...};

[0041] An output module that extracts three consecutive range cells from the sequence L of affected range cells to perform a fast Fourier transform, selects the sequence l of affected range cells with the largest product, s extracts the waveform of the sequence l s and performs smoothing to obtain the preprocessed respiratory signal.

[0042] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for preprocessing respiratory monitoring signals in a user movement scenario are implemented.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for preprocessing breathing monitoring signals in a user movement scenario are implemented.

[0044] In a fifth aspect, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for preprocessing breathing monitoring signals in a user movement scenario are implemented.

[0045] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, and when the computer program is executed by the electronic device, the steps of the above-mentioned method for preprocessing breathing monitoring signals in a user movement scenario are implemented.

[0046] Compared with the prior art, the present invention has at least the following beneficial effects:

[0047] A method for preprocessing breathing monitoring signals in a user movement scenario effectively solves the non-uniformity problems of signals in the range domain and time domain through compensation of signal amplitude and phase, background elimination, and reasonable selection of affected sequences, and can accurately eliminate background noise in a complex environment, improving the clarity and stability of breathing signals.

[0048] Furthermore, the method of monitoring breathing by processing signals collected by an ultra-wideband radar can accurately locate the signals of breathing activities in a relatively noisy environment. Non-contact breathing monitoring using a commercial impulse radio ultra-wideband (IR-UWB) radar can reduce the time cost and labor cost brought by sensor pre-deployment, and can support simultaneous monitoring of multiple people at a long distance.

[0049] Furthermore, compensation for the variation with range cells in the signal can effectively reduce the amplitude and phase deflection caused by signal variation with distance, and effectively reduce the inconsistency of background reflections at different distances.

[0050] Furthermore, by double-clustering the amplitude and phase in each range cell of the signal and then subtracting the average value, the problem of non-uniform perception in the signal can be effectively solved, and the dynamic and complex environmental conditions can be effectively adapted to.

[0051] Furthermore, the tracking of affected range cells is modeled as a directed probabilistic graph model, comprehensively considering three key factors: the amplitude similarity between consecutive time points, the proximity of range cells, and the magnitude of the overall amplitude sum, ensuring a robust and accurate selection of the next affected range cell, and effectively selecting the candidate range cell sequence affected by breathing.

[0052] It is understandable that the beneficial effects of the above second to sixth aspects can be referred to the relevant descriptions in the above first aspect, and will not be repeated here.

[0053] In summary, the method of the present invention does not rely on specific environmental conditions and prior knowledge, has strong adaptability and versatility, and can be widely applied to various breathing monitoring scenarios based on radio frequency signals, such as smart home, medical monitoring and other fields, providing strong support for realizing high-precision breathing monitoring.

[0054] Next, through the drawings and embodiments, the technical solutions of the present invention will be further described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0056] Figure 1 is the working principle diagram of the IR-UWB radar;

[0057] Figure 2 is the process diagram of adaptive dual-domain background elimination;

[0058] Figure 3 is the process diagram of breathing recognition and tracking;

[0059] Figure 4 is the comparison diagram with other similar methods;

[0060] Figure 5 is the flow diagram of the present invention;

[0061] Figure 6 is the schematic diagram of the computer device provided by an embodiment of the present invention;

[0062] Figure 7 is the block diagram of an electronic device provided by an embodiment of the present invention.

[0063] Among them, 60. computer device; 61. processor; 62. memory; 63. computer program; 600. electronic device; 610. processing unit; 620. storage unit; 6201. random access storage unit; 6202. cache storage unit; 6203. read-only storage unit; 6204. program / utilities; 6205. program module; 630. bus; 640. display unit; 650. input / output interface; 660. network adapter; 700. external device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part rather than all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] In the description of the present invention, it should be understood that the terms "include" and "comprise" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0066] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0067] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the contextually related objects.

[0068] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe preset ranges, etc., these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0069] Depending on the context, the word "if" as used herein can be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" can be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0070] Various structural schematic diagrams according to the disclosed embodiments of the present invention are shown in the accompanying drawings. These figures are not drawn to scale, where for the purpose of clear expression, some details are enlarged and some details may be omitted. The shapes of various regions and layers shown in the figures, as well as their relative sizes and positional relationships, are merely exemplary, and in practice, there may be deviations due to manufacturing tolerances or technical limitations. And those skilled in the art can design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0071] The present invention provides a method for preprocessing respiratory monitoring signals in a user movement scenario. A commercial impulse radio ultra-wideband (IR-UWB) radar is used to sense respiratory events, distance unit compensation is performed on the signal matrix, background reflection elimination is performed both in the distance domain and the time domain, and then a method for tracking affected distance units is designed, so as to select a candidate sequence of affected units to effectively restore the respiratory signal, which can be widely applied to fields such as smart home and smart healthcare.

[0072] Embodiment 1

[0073] Please refer to Figure 5 , a method for preprocessing respiratory monitoring signals in a user movement scenario according to the present invention includes the following steps:

[0074] S1. Collect signal data to form an original signal matrix Y(t, i), and compensate for the difference by multiplying an attenuation estimation parameter to reduce the influence of the changing environment on the signal, and the obtained signal is denoted as Y′ t (t, i);

[0075] Due to the amplitude attenuation and phase rotation caused by distance, the received signals Y(t, i) on different distance units exhibit different characteristics, which poses a challenge to unified signal processing. Therefore, by multiplying an attenuation estimation parameter to compensate for this difference:

[0076]

[0077] where, A c =(i·Δd) 2 , θ c =2πf c ·(i·Δd) / c, Y t (i)=Y(t, i)| t＝t,i＝1→N , N represents the total number of distance units, Δd represents the distance difference between adjacent distance units, f c is the carrier frequency, c is the speed of light, Y t (i) represents the signal received in the t-th frame, and Y′ t (i) is the compensated signal;

[0078] Please refer to Figure 1 , where t is the index of the transmitted frame, and i represents the distance interval, and each interval corresponds to the distance resolution of the radar.

[0079] Please refer to Figure 2 , as shown in the compensation result, the signal matrix Y is compensated from the original signal matrix Y ′ , and the range cells at longer distances are compensated.

[0080] S2. For the signal matrix Y ′ (t, i) obtained in step S1, an improved adaptive clutter cancellation algorithm is used to adaptively estimate the background reflection varying with time and eliminate it, and the obtained signal is denoted as Y * (t, i);

[0081] The method for updating the background estimation using the improved adaptive clutter cancellation algorithm is as follows:

[0082]

[0083]

[0084] where 0 < λ L < λ H < 1 is the forgetting factor, σ i (t) is the adaptive coefficient, and Y * (t, i) is the finally obtained signal;

[0085] Please refer to Figure 2 , the signal matrix Y is obtained by eliminating the time-domain background reflection from the signal matrix Y′ * , and the clutter is further eliminated, and the affected cells become clearer.

[0086] S3. For each range cell in the signal matrix Y * (t, i) obtained in step S2, DBSCAN clustering is performed separately on the amplitude and phase, and the average amplitude and average phase of the dominant class are subtracted from the original signal matrix to eliminate the background reflection in the range space, and the obtained signal is denoted as Y"(t, i);

[0087] The signal vector is decomposed into an amplitude vector and a phase vector DBSCAN clustering is performed separately on these two vectors for each range cell, and the average amplitude μ A and the average phase μ Φ are calculated as follows:

[0088]

[0089] where and represent the amplitude matrix A * and the phase matrix Φ * corresponding to the dominant category of the i-th range cell in

[0090] Then, through the formula eliminate the background reflection in the range space, where ⊙ represents dot product and ∈ is a small constant to prevent division-by-zero errors, represents the signal after background elimination;

[0091] Please refer to Figure 2 to obtain the signal matrix * by eliminating the background reflection in the range domain from the signal matrix Y The background reflection is basically completely eliminated, and the sequence of affected cells is clearly visible.

[0092] S4. Model the tracking problem of the affected range cells as a directed probabilistic graph model, generate a cost matrix for calculating this directed probabilistic graph problem using the signal Y″(t, i) obtained in step S3, and finally obtain the sequence l of affected range cells c , and use the Savitzky-Golay filter to smooth the sequence;

[0093] Select the next affected range cell through the formula where where t and t + 1 are frame numbers, is the serial number of the next affected range cell, represents the cost matrix from the range cell i in the t-th frame t to the range cell i in the (t + 1)-th frame ′ ;

[0094] Please refer to Figure 3 to obtain two candidate sequences as shown in the figure: sequence 1 (red sequence) and sequence 2 (yellow sequence) by tracking the sequence of affected range cells from the signal matrix ;

[0095] S5. Since multipath effects are prevalent in most indoor environments, step S4 may identify a set of sequences of affected range cells L = {l1,…,l c ,…}. According to the sequence L of affected range cells obtained in step S4, perform a fast Fourier transform on three consecutive range cells extracted, and select the sequence l of affected range cells with the largest product s . Extract the waveform of the sequence l s and smooth it using a Butterworth filter to obtain the final preprocessed respiration signal.

[0096] First, extract the respiration waveform Then perform a fast Fourier transform on it; then, extract the waveform of sequence l s and smooth it using a Butterworth filter to obtain the final preprocessed respiratory signal.

[0097] Since human respiration affects several adjacent range cells, multiply the fast Fourier transform results F(·) of three consecutive range cells to highlight the common frequency components and suppress noise simultaneously, that is:

[0098]

[0099] where, V is the coefficient corresponding to respiration, represents the noise component. Since noise has nothing to do with human respiration, the noise component is almost negligible.

[0100] In contrast, due to the existence of common respiratory components, will exhibit significant non-zero coefficients. Select the sequence l of the affected range cells with the largest frequency product Ψ s for subsequent processing.

[0101] Please refer to Figure 3 . Since the frequency product of sequence 2 is greater than that of sequence 1 among the two candidate sequences, sequence 2 is selected and its waveform is extracted as shown in the figure.

[0102] Those skilled in the art can understand that various aspects of the present invention can be implemented as a system, method, or program product. Therefore, various aspects of the present invention can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuit", "module", or "platform" here.

[0103] Embodiment 2

[0104] The present invention provides a preprocessing system for respiratory monitoring signals in a user movement scenario. This system can be used to implement the above-mentioned preprocessing method for respiratory monitoring signals in a user movement scenario. Specifically, the preprocessing system for respiratory monitoring signals in a user movement scenario includes an acquisition module, a clustering module, a calculation module, and an output module.

[0105] Among them, the acquisition module acquires signal data and forms a signal matrix Y(t, i);

[0106] The preprocessing module preprocesses the obtained signal matrix Y(t, i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y ′ (t, i);

[0107] The clustering module performs DBSCAN clustering on each distance cell in the signal matrix Y ′ (t, i), and subtracts the average amplitude and average phase of the dominant category from the original signal matrix to eliminate background reflections in the distance space, obtaining the signal Y″(t, i);

[0108] The calculation module models the tracking problem of the affected distance cells as a directed probabilistic graph model, generates a cost matrix from the obtained signal Y″(t, i), calculates the tracking problem, and generates a sequence l of affected distance cells c , for the sequence l of affected distance cells c Perform smoothing processing to obtain a set of sequences L of affected distance cells = {l1,…,l c ,…};

[0109] The output module extracts three consecutive distance cells from the sequence L of affected distance cells to perform a fast Fourier transform, selects the sequence l of affected distance cells with the largest product s , extracts the waveform of the sequence l s And perform smoothing processing to obtain a preprocessed respiratory signal.

[0110] Example 3

[0111] The present invention provides a terminal device, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Unit (GPU), Tensor Processing Unit (TPU), Digital Signal Processor (DSP), Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiments of the present invention can be used for the operation of the method for preprocessing respiratory monitoring signals in the user mobile scenario, including:

[0112] Collect signal data and form a signal matrix Y(t,i); preprocess the obtained signal matrix Y(t,i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y ′ (t,i); perform DBSCAN clustering on each range bin in the signal matrix Y ′ (t,i), and subtract the average amplitude and average phase of the dominant class from the original signal matrix to eliminate background reflections in the range space, obtaining a signal Y″(t,i); model the tracking problem of the affected range bins as a directed probabilistic graph model, generate a cost matrix from the obtained signal Y ′′ (t,i), calculate the tracking problem and generate a sequence l of affected range bins c , perform smoothing on the sequence l of affected range bins c to obtain a set of sequences L of affected range bins = {l1,…,l c ,…}; extract three consecutive range bins from the sequence L of affected range bins to perform a fast Fourier transform, select the sequence l of affected range bins with the largest product s , extract the waveform of the sequence l s and perform smoothing to obtain a preprocessed respiratory signal.

[0113] Please refer to Figure 6 , the terminal device is a computer device. The computer device 60 in this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, it implements the method for preprocessing respiratory monitoring signals in the user movement scenario of the embodiment. To avoid repetition, it will not be described in detail here. Alternatively, when the computer program 63 is executed by the processor 61, it implements the functions of each model / unit in the preprocessing system for respiratory monitoring signals in the user movement scenario of the embodiment. To avoid repetition, it will not be described in detail here.

[0114] The computer device 60 can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art can understand that Figure 6 merely examples of the computer device 60, which do not constitute a limitation on the computer device 60, and may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0115] The so-called processor 61 may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0116] The memory 62 may be an internal storage unit of the computer device 60, such as the hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk equipped on the computer device 60, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc.

[0117] Furthermore, the memory 62 may also include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0118] Please refer to Figure 7 , the terminal device is the electronic device 600, and the electronic device 600 is presented in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0119] Among them, the storage unit stores program code, and the program code can be executed by the processing unit 610, so that the processing unit 610 executes the steps according to various exemplary embodiments of the present invention described in the method part of this specification. For example, the processing unit 610 may execute the steps as shown in Figure 5 .

[0120] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only storage unit (ROM) 6203.

[0121] The storage unit 620 may also include a program / utilities 6204 having a set (at least one) of program modules 6205. Such program modules 6205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0122] The bus 630 may represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0123] The electronic device 600 may also communicate with one or more external devices 700 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or may communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (such as a router, a modem). Such communication may be through the input / output interface 650. Also, the electronic device 600 may communicate with one or more networks (such as a local area network, a wide area network, and / or a public network, such as the Internet) through the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 through the bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0124] Embodiment 4

[0125] The present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device and is used to store programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and, of course, the extended storage medium supported by the terminal device. It can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. And, in this storage space, there are also stored one or more instructions suitable for being loaded and executed by a processor, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples of the computer-readable storage medium here include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0126] The computer-readable storage medium also includes data signals propagated in a baseband or as part of a carrier wave, which carry the readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency, etc., or any suitable combination of the above.

[0127] The program code for performing the operations of the present invention can be written in any combination of one or more programming languages. The programming languages include object-oriented programming languages - such as Java, C++, etc., and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as an independent software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network or a wide area network, or can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0128] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the method for preprocessing respiratory monitoring signals in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0129] Collect signal data and form a signal matrix Y(t, i); preprocess the obtained signal matrix Y(t, i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y ′ (t, i); perform DBSCAN clustering on each range bin in the signal matrix Y ′ (t, i), and subtract the average amplitude and average phase of the dominant class from the original signal matrix to eliminate background reflections in the range space, obtaining a signal Y″(t, i); model the tracking problem of the affected range bins as a directed probabilistic graph model, generate a cost matrix from the obtained signal Y ′′ (t, i), calculate the tracking problem and generate a sequence l of affected range bins c , and smooth the sequence l of affected range bins c to obtain a set of sequences L of affected range bins = {l1,..., l c ,...}; extract three consecutive range bins from the sequence L of affected range bins to perform a fast Fourier transform, select the sequence l of affected range bins with the largest product s , extract the waveform of the sequence l s and smooth it to obtain a preprocessed respiratory signal.

[0130] The databases involved in the embodiments provided by this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., and is not limited thereto. The processors involved in the embodiments provided by this application may be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., and are not limited thereto.

[0131] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components described and shown in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0132] The present invention is a method for preprocessing respiratory monitoring signals in a user's mobile scenario based on radio frequency signals. Experiments are conducted in different environments:

[0133] Please refer to Figure 4 , and evaluate the performance when participants walk freely in five different scenarios, including: walking in an empty laboratory, walking in a crowded laboratory, walking in a corridor, walking around obstacles, and walking in a hospital ward.

[0134] The average cosine similarity of the present invention is 0.8481, which is better than other methods (the average cosine similarity of AUG is 0.8011, MoRe-Fi is 0.5164, RF-Carer*+VED is 0.5171, and AUG+RF-Carer is 0.8402). This indicates that the present invention is effective in processing and obtaining respiratory signals in free movement situations in different scenarios.

[0135] In summary, a method and system for preprocessing respiratory monitoring signals in a user's mobile scenario according to the present invention uses a commercial impulse radio ultra-wideband (IR-UWB) radar to achieve preprocessing of respiratory monitoring signals in a user's mobile scenario; it does not involve privacy issues, does not require pre-deployment of sensors, avoids the limitations of user scenarios in existing respiratory signal preprocessing schemes, and achieves high-precision recognition and restoration of respiratory signals.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be assigned to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other and do not limit the protection scope of this application. The specific working processes of the units and modules in the above system can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0137] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0138] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in the form of hardware or software depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0139] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal and method can be implemented in other ways. For example, the device / terminal embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0142] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0143] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses, and computer program products according to embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the specified function in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0144] These computer program instructions may also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device implements the process in Figure 1One process or multiple processes and / or boxes Figure 1 The functions specified in one box or multiple boxes.

[0145] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 One process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one box or multiple boxes.

[0146] The above content is only to illustrate the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the claims of the present invention.

Claims

1. A method for preprocessing respiratory monitoring signals in a user's mobile scenario, characterized in that, Including the following steps: Collect signal data and form a signal matrix Y(t, i); Preprocess the signal matrix Y(t, i) using an improved adaptive clutter cancellation algorithm to obtain a signal matrix Y′(t, i); Perform DBSCAN clustering on each range cell in the obtained signal matrix Y′(t, i), and subtract the average amplitude and average phase of the dominant class from the original signal matrix to eliminate background reflections in the range space, obtaining a signal Y″(t, i); Model the tracking problem of the affected range cells as a directed probabilistic graph model, generate a cost matrix for the obtained signal Y″(t,i), calculate the tracking problem, and generate a sequence l of affected range cells c , for the sequence l of affected range cells c perform smoothing to obtain a set of sequences L of affected range cells = {l1,…,l c ,…}; Perform fast Fourier transform on three consecutive distance units in the obtained affected distance unit sequence L, and select the affected distance unit sequence l with the largest product. s , extract the sequence l s 's waveform and perform smoothing processing to obtain the preprocessed respiratory signal.

2. The method for preprocessing respiratory monitoring signals in a user movement scenario according to claim 1, wherein Specifically, collecting signal data and forming a signal matrix Y(t, i) is as follows: Collect signal data using an IR-UWB radar and compensate for the difference by multiplying the attenuation estimation parameter A c is the amplitude compensation estimation parameter between different range cells, j is the imaginary unit, and θ c is the phase compensation estimation parameter between different range cells, and the compensated signal Y′ t (i) is obtained, and finally the signal matrix Y(t, i) is obtained.

3. The method for preprocessing respiration monitoring signals in a user movement scenario according to claim 2, wherein, Compensated signal matrix Y' t (i) is as follows: Among them, Y t (i) represents the signal received in the t-th frame.

4. The method for preprocessing respiratory monitoring signals in a user movement scenario according to claim 2, wherein The signal Y received in the t-th frame t (i) is: Y t (i) = Y(t, i)| t＝t,i＝1→N Where N represents the total number of range cells, t is the index of the transmitted frame, and i represents the range interval.

5. The method for preprocessing respiratory monitoring signals in the user movement scenario according to claim 1, characterized in that For the signal matrix Y ′ Performing DBSCAN clustering on each range cell in (t, i) specifically involves: Decompose the signal vector into an amplitude vector and a phase vector Perform DBSCAN clustering on these two vectors for each range bin respectively, and calculate the average amplitude μ A and the average phase μ Φ ; Then eliminate the background reflection in the range space, starting from the signal matrix Y * Eliminate the background reflection in the range domain to obtain the signal matrix Y″.

6. The method for preprocessing a respiration monitoring signal in a user movement scenario according to claim 5, characterized in that, Average amplitude μ A and average phase μ Φ are as follows: Among them, and respectively represent the dominant categories corresponding to the \(i\)-th range bin in the amplitude matrix \(A\) * and the phase matrix \(\varPhi\) * , \(N\) represents the total number of range bins, represents the average amplitude of the data with clustering label \(t\) when the range bin is \(i\), represents the average phase of the data with clustering label \(t\) when the range bin is \(i\).

7. The method for preprocessing respiratory monitoring signals in a user movement scenario according to claim 5, wherein, Eliminating background reflections in the range space is specifically as follows: Among them, ⊙ represents dot product, and ∈ represents a small constant to prevent division-by-zero errors. represents the signal after background elimination, Y * (t, i) is the signal obtained by using the improved adaptive clutter cancellation algorithm.

8. The method for preprocessing respiratory monitoring signals in a user movement scenario according to claim 1, wherein Generate a cost matrix from the obtained signal Y″(t, i), specifically: The tracking of affected range cells is modeled as a directed probabilistic graphical model, via Select the next affected distance cell, t and t+1 are frame numbers, is the next affected distance unit number, Represents the distance unit i from the tth frame t Distance unit i to frame t+1 ′ The cost matrix of .

9. The method for preprocessing respiratory monitoring signals in the user movement scenario according to claim 1, wherein, Specifically, obtaining the preprocessed respiratory signal is as follows: Extract the respiratory waveform Then perform a fast Fourier transform; then extract the waveform of sequence l s and smooth it using a Butterworth filter to obtain the final preprocessed respiratory signal; The fast Fourier transform is as follows: Among them, V represents a coefficient corresponding to respiration, represents a noise component.

10. A preprocessing system for breathing monitoring signals in a user's movement scenario, characterized in that Including: A collection module that collects signal data and forms a signal matrix Y(t, i); The preprocessing module preprocesses the obtained signal matrix Y(t,i) using an improved adaptive clutter cancellation algorithm to obtain the signal matrix Y ′ (t,i); Clustering module, for signal matrix Y ′ Perform DBSCAN clustering on each range cell in (t,i), and subtract the average amplitude and average phase of the dominant class from the original signal matrix to eliminate the background reflection in the range space, obtaining signal Y″(t,i); A calculation module models the tracking problem of affected distance units as a directed probabilistic graph model, generates a cost matrix from the obtained signal Y″(t,i), calculates the tracking problem, and generates a sequence l of affected distance units c , for the sequence l of affected distance units c perform smoothing to obtain a set of sequences L of affected distance units L = {l1, …, l c , …}; The output module extracts three consecutive distance units from the sequence L of affected distance units for fast Fourier transform and selects the sequence l of affected distance units with the largest product. s , extracts the sequence l s 's waveform and performs smoothing processing to obtain the preprocessed respiratory signal.

Images